Rainfall is a problem in automated traffic surveillance. Rain streaks occlude the road users and degrade the overall visibility which in turn decrease object detection performance. One way of alleviating this is by artificially removing the rain from the images. This requires knowledge of corresponding rainy and rain-free images. Such images are often produced by overlaying synthetic rain on top of rain-free images. However, this method fails to incorporate the fact that rain fall in the entire three-dimensional volume of the scene. To overcome this, we introduce training data from the SYNTHIA virtual world that models rain streaks in the entirety of a scene. We train a conditional Generative Adversarial Network for rain removal and apply it on traffic surveillance images from SYNTHIA and the AAU RainSnow datasets. To measure the applicability of the rain-removed images in a traffic surveillance context, we run the YOLOv2 object detection algorithm on the original and rain-removed frames. The results on SYNTHIA show an 8% increase in detection accuracy compared to the original rain image. Interestingly, we find that high PSNR or SSIM scores do not imply good object detection performance.

Extisting biodiversity databases contain an abundance of information. To turn such information into knowledge, it is necessary to address several information-model issues. Biodiversity data are collected for various scientific objectives, often even without clear preliminary objectives, may follow different taxonomy standards and organization logic, and be held in multiple file formats and utilising a variety of database technologies. This paper presents a graph catalogue model for the metadata management of biodiversity databases. It explores the possible operation of data mining and visualization to guide the analysis of heterogeneous biodiversity data. In particular, we would propose contributions to the problems of (1) the analysis of heterogeneous distributed data found across different databases, (2) the identification of matches and approximations between data sets, and (3) the identificaton of relationships between various databases. This paper describes a proof of concept of an infrastructure testbed and its basic operations, presenting an evaluation of the resulting system in comparison with the ideal expectations of the ecologist.

One of the benefits of OCCI stems from simplifying the life of developers aiming to integrate multiple cloud managers. It provides them with a single protocol to abstract the differences between cloud service implementations used on sites run by different providers. This comes particularly handy in federated clouds, such as the EGI Federated Cloud Platform, which bring together providers who run different cloud management platforms on their sites: most notably OpenNebula, OpenStack, or Synnefo. Thanks to the wealth of approaches and tools now available to developers of virtual resource management solutions, different paths may be chosen, ranging from a small-scale use of an existing command line client or single-user graphical interface, to libraries ready for integration with large workload management frameworks and job submission portals relied on by large science communities across Europe. From lone wolves in the long-tail of science to virtual organizations counting thousands of users, OCCI simplifies their life through standardization, unification, and simplification. Hence cloud applications based on OCCI can focus on user specifications, saving cost and reaching a robust development life-cycle. To demonstrate this, the paper shows several EGI Federated Cloud experiences, demonstrating the possible approaches and design principles.

Deformation of expressive textures is the gateway to realistic computer synthesis of expressions. By their good mathematical properties and flexible formulation on irregular meshes, most texture mappings rely on solutions to the Laplacian in the cartesian space. In the context of facial expression morphing, this approximation can be seen from the opposite point of view by neglecting the metric. In this paper, we use the properties of the Laplacian in manifolds to present a novel approach to warping expressive facial images in order to generate a morphing between them.

The aim of this paper is to present the research of the first author, which aims to seek for a methodology to address control problems by implementing Multi-Objective Optimization Design (MOOD) procedure. This procedure consist of at least of three elements: the definition of a multiobjective problem, the simultaneous optimization of all objectives for the generation of a set of Pareto-optimal solutions and a process of multi-criteria decision making to select the most preferable solution to implement. Up to now, the research has been applied to tuning application for PI/PID controller design. The research problem, objectives, the methodology, some of the contributions and the stage of the research are detailed below. Also, some implementations and current development work is discussed

Topics: Decision Support Systems; ICT, Ageing and Disability; Design and Development Methodologies for Healthcare IT; Mobile Technologies for Healthcare Applications; Evaluation and Use of Healthcare IT

Today, the healthcare monitoring is not limited to take place in primary care facilities simply due to
deployment of ICT. However, to support an ICT-based health monitoring, proper health parameters, sensor
devices, data communications, approaches, methods and their combination are still open challenges. This
paper presents a self-serve ICT-based health monitoring system to support active ageing by assisting seniors
to participate in regular monitoring of elderly’s health condition. Here, the main objective is to facilitate a
number of healthcare services to enable good health outcomes of healthy active living. Therefore, the
proposed approach is identified and constructed three different kinds of healthcare services: 1) real time
feedback generation service, 2) historical summary calculation service and 3) recommendation generation
service. These services are implemented considering a number of health parameters, such as, 1) blood
pressure, 2) blood glucose, 3) medication compliance, 4) weight monitoring, 5) physical activity, 6) pulse
monitoring etc. The services are evaluated in Spain and Slovenia through 2 prototypical systems, i.e.
year2prototype (Y2P) and year3prototype (Y3P) by 46 subjects (40 for Y2P and 6 for Y3P). The evaluation
results show the necessity and competence of the proposed healthcare services. In addition, the prototypical
system (i.e. Y3P) is found very much accepted and useful by most of the users.

This paper presents details of a general purpose micro-task on-demand platform based on the crowdsourcing
philosophy. This platform was specifically developed for mobile devices in order to exploit the strengths of
such devices; namely: i) massivity, ii) ubiquity and iii) embedded sensors. The combined use of mobile
platforms and the crowdsourcing model allows to tackle from the simplest to the most complex tasks. Users
experience is the highlighted feature of this platform (this fact is extended to both task-proposer and tasksolver).
Proper tools according with a specific task are provided to a task-solver in order to perform his/her job
in a simpler, faster and appealing way. Moreover, a task can be easily submitted by just selecting predefined
templates, which cover a wide range of possible applications. Examples of its usage in computer vision and
computer games are provided illustrating the potentiality of the platform.

Nowadays, different eScience actors are assuming the Federated Cloud as a model for the aggregation of distributed cloud resources. In this complex environment, service delivery and resource usage is an open problem, where multiple users and communities are committed to particular policies while using federated resources. In the other hand, DIRAC has become a multi-community middleware fully interoperable in Distributed Computing Infrastructures (DCI), including several cloud managers. Furthermore, DIRAC is able to federate Infrastructure as a Service (IaaS) to provide Software as a Service (SaaS) in a transparent manner to the final user. This paper defines a credit model for the resource usage providing automatic management in federated cloud. It is presented a prototype of this model, which is implemented with DIRAC, describing a test for the model assessment and drawing up conclusions for a production level federated cloud governance.

This manuscript addresses the cross-spectral stereo correspondence problem. It proposes the usage of a dense
flow field based representation instead of the original cross-spectral images, which have a low correlation. In
this way, working in the flow field space, classical cost functions can be used as similarity measures. Preliminary
experimental results on urban environments have been obtained showing the validity of the proposed
approach.

Quick Response (QR) codes, used to store machine readable information, have become very common nowadays and have found many applications in different scenarios. One of such applications is electronic voting systems. Indeed, some electronic voting systems are starting to take advantage of these codes, e.g. to hold the ballots used to vote, or even as a proof of the voting process. Nevertheless, QR codes are susceptible to steganographic techniques to hide information. This steganographic capability enables a covert channel that in
electronic voting systems can suppose an important threat. A misbehaving equipment (e.g. infected with malware) can introduce hidden information in the QR code with the aim of breaking voters’ privacy or enabling coercion and vote-selling. This paper shows a method for hiding data inside QR codes and an implementation of a QR writer/reader application with steganographic capabilities. The paper analyses different possible attacks to electronic voting systems that leverage the steganographic properties of the QR codes. Finally, it proposes some solutions to detect the mentioned attacks.